Results

1. THE ROLE OF ALCOHOL USE DISORDER IN NORMATIVE CHANGES IN NOVELTY SEEKING DURING YOUNG ADULTHOOD Jenny M. Larkins, Emily R. Grekin, Julia A. Martinez, and Kenneth J. Sher Department of Psychological Sciences, University of Missouri-Columbia, and the Midwest Alcoholism Research Center Figure 1. AUD Groups from Cluster Analysis • Introduction • Most research on personality development has shown a mean decrease in disinhibited personality and behavioral undercontrol during young adulthood. • However, some individuals do not show this decrease. • Sher et al. (2004) proposed that substance use disorders can cause a developmental lag, such that individuals with these disorders can be delayed in achieving the age-related decrease in behavioral undercontrol. • The mechanism of this delay has been described as a developmental snare, in which substance use disorders inhibit normative declines in psycholopathology and problem behavior (Hussong et al., 2004). Purpose • The purpose of the current analyses was to examine the role of alcohol use disorders (AUDs; alcohol abuse or dependence) in the change in disinhibited personality during young adulthood. • Novelty Seeking was chosen because, among measures of behavioral undercontrol in the current study, it shows the largest age-related decrease. Sample • Initial sample of first-year college students (Year 1) • N=489 (47% men; 51% with family history of alcoholism) • Mean age=18.2 years (SD=0.96) • Assessed with self-report questionnaires and interview • Six follow-up assessments (Years 2, 3, 4, 7, 11, and 16) • 340 participants (70% of the Year 1 sample) provided complete data at all time points. • Measures • Novelty Seeking • Measured by the short version of the Tridimensional • Personality Scale (Cloninger, 1987; Sher et al., 1995) • Administered at Years 1, 7, 11, and 16 of the study • Past-year Alcohol Use Disorders • DSM-III diagnoses as measured by the Diagnostic Interview Schedule (DIS-III-A [Robins et al., 1985]) • Assessed at all time points Analyses • To find heterogeneous growth patterns of AUDs, a cluster analytic technique was used to classify subjects. • The clustering procedure employed is based on traditional K-means clustering, but differs in that the longitudinal nature of the data is accounted for (Steinley et al., 2006). • Repeated measures ANOVA • Overall tests of time, AUD group, and sex effects, between-subjects contrasts, and profile contrasts Results • The cluster analysis resulted in a four-group solution, chosen based on maximizing the variance (53% in the present solution) accounted for relative to the number of groups. • The four groups are shown in Figure 1. • There were significant effects of time and AUD group on Novelty Seeking scores. • The sex, sex X AUD group, and time X AUD group effects were all nonsignificant. • Least squares means are shown in Figure 2, and the results from the repeated-measures ANOVA are • presented in Table 1. • Between-subjects contrasts compared a priori groups over the course of the study (effects shown in Table 2) • Non-diagnosers vs. all others (p < .0001) • Remitters vs. all others (p=.0001) • Late onset vs. all others (nonsignificant) • Profile contrasts compared the a priori groups at specific time intervals (effects shown in Table 3) • Late onset vs. all others (nonsignificant for all intervals) • Remitters vs. all other (significantly different between Years 7 and 11 of the study) • Late onset vs. all others (nonsignificant for all intervals) • Conclusions • AUD groups, based on cluster analyses, predicted Novelty Seeking scores over the course of the study. • Individuals who did not diagnose at any time point consistently had the lowest levels of Novelty Seeking, whereas the Remitters had the highest levels. • In general, Novelty Seeking decreased over time. • We found evidence for a developmental delay in achieving normative levels of Novelty Seeking among the Remitters such that their Novelty Seeking scores did not stabilize until later as compared to the other AUD groups. Figure 2. Short TPQ Novelty Seeking Scores Note. Means shown are controlling for sex. Table 1. Predictors of Novelty Seeking References Table 2. Between-SubjectContrast Analyses Cloninger, C. R. (1987b). Tridimensional Personality Questionnaire, Version 4. Unpublished manuscript, Washington University, St. Louis, MO. Hussong, A. M., Curran, P. J., Moffitt, T. E., Caspi, A., & Carrig, M. M. (2004). Substance abuse hinders desistance in young adults’ antisocial behavior. Development and Psychopathology, 16, 1029-1046. Robins, L. N., Helzer, J. E., Croughan, J., Williams, J. B. W., & Spitzer, R. L. (1985). National Institute of Mental Health Diagnostic Interview Schedule, Version III-A. Public Health Service: Washington, D. C. Sher, K. J., Gotham, H. J., & Watson, A. L. (2004). Trajectories of dynamic predictors of disorder: Their meanings and implications. Development and Psychopathology, 16, 825-856. Sher, K. J., Wood, M. D., Crews, T. M., & Vandiver, P. A. (1995). The Tridimensional Personality Questionnaire: Reliability and validity studies and derivation of a short form. Psychological Assessment, 7, 195-208. Steinley, D., Jahng, S., Wood, P. K., & Brusco, M. J. (2006).  Initializing growth mixture models. Manuscript in preparation. Table 3. ProfileContrast Analyses Supported by grants R37 AA7231 and T32 AA13526 to Kenneth J. Sher and P50 AA11998 to Andrew C. Heath. Thank you to Doug Steinley for assistance with the cluster analysis. Note. *p < .01; All df=1.